RoiSet facilitates object detection models

• created distinct generator class methods for RoiSet
• RoiSet.from_bounding_boxes can create RoiSet with just YXWH coordinates and no segmentation, e.g. as output from object detection models
• can also inspect and quantify overlap between and within RoiSet(s), at both bounding box and segmentation level
• can generate RoiSet from polygon segmentation e.g. in COCO segmentation models

model_server/base/roiset.py

@@ -142,10 +142,10 @@ def filter_df_overlap_bbox(df: pd.DataFrame) -> pd.DataFrame:
             second.append(pair[0])
 
     sdf = df.iloc[first]
-    sdf['bbox_overlaps_with'] = second
+    sdf.loc[:, 'bbox_overlaps_with'] = second
     return sdf
 
-# TODO: get overlapping segments
+
 def filter_df_overlap_seg(df: pd.DataFrame) -> pd.DataFrame:
     """
     Return subset of DataFrame whose segmentations overlap in 3D space.
@@ -153,9 +153,9 @@ def filter_df_overlap_seg(df: pd.DataFrame) -> pd.DataFrame:
 
     dfbb = filter_df_overlap_bbox(df)
 
-    def _overlap_seg(roi_i):
-        roi1 = roi_i
-        roi2 = dfbb.loc[roi_i.bbox_overlaps_with]
+    def _overlap_seg(r):
+        roi1 = df.loc[r.name]
+        roi2 = df.loc[r.bbox_overlaps_with]
         ex0 = min(roi1.x0, roi2.x0, roi1.x1, roi2.x1)
         ew = max(roi1.x0, roi2.x0, roi1.x1, roi2.x1) - ex0
         ey0 = min(roi1.y0, roi2.y0, roi1.y1, roi2.y1)
@@ -167,8 +167,7 @@ def filter_df_overlap_seg(df: pd.DataFrame) -> pd.DataFrame:
         emask[sl2] = emask[sl2] + roi2.binary_mask
         return np.any(emask > 1)
 
-    se = dfbb.apply(_overlap_seg, axis=1)
-    dfbb['seg_overlaps_with'] = se
+    dfbb['seg_overlaps'] = dfbb.apply(_overlap_seg, axis=1)
     return dfbb
 
 def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by) -> pd.DataFrame: